run.all.lms: Function that will run a regression for your variable of...

In syyang93/analyzeR: This package contains functions that Stephanie uses for her analysis of recount RNAseq data.

Description

Function that will run a regression for your variable of interest across all genes, in parallel. Adapted from Vamsee (github.com/vkp3/pillalamarRi)

Usage

run.all.lms(tx_expr, cov, gene.ids, SCORE, omit.outlier = T,
  num.cores = 10)

Arguments

tx_expr	Expression matrix in form: [genes x samples]. Will be converted to a list(!) of gene-expr vectors (if not input as list)
cov	Regression covariates [cov x samples]
gene.ids	Character vector of gene IDs, corresponding to rows in 'tx_expr' [genes x samples]
SCORE	Main covariate to be permuted (not included in 'cov')
omit.outlier	Whether or not you want to omit gene expression outliers
num.cores	The number of cores you would like to use

Value

All regression coefficients for lm(gene expression ~ SCORE + cov) for all genes

Examples

lm_res.sort <- run.all.lms(my.list[[1]], my.list[[2]], my.list[[3]], my.list[[4]], omit.outlier = T, num.cores = 10)

syyang93/analyzeR documentation built on Aug. 26, 2020, 4:34 p.m.